UNEMPLOYMENT ANALYSIS USING PYTHON¶

Importing Libraries

In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

Reading CSV file

In [2]:
data=pd.read_csv("Unemployment_Rate_upto_11_2020.csv")

Describing Data

In [3]:
print(data.head())

           Region         Date  Frequency   Estimated Unemployment Rate (%)  \
0  Andhra Pradesh   31-01-2020          M                              5.48   
1  Andhra Pradesh   29-02-2020          M                              5.83   
2  Andhra Pradesh   31-03-2020          M                              5.79   
3  Andhra Pradesh   30-04-2020          M                             20.51   
4  Andhra Pradesh   31-05-2020          M                             17.43   

    Estimated Employed   Estimated Labour Participation Rate (%) Region.1  \
0             16635535                                     41.02    South   
1             16545652                                     40.90    South   
2             15881197                                     39.18    South   
3             11336911                                     33.10    South   
4             12988845                                     36.46    South   

   longitude  latitude  
0    15.9129     79.74  
1    15.9129     79.74  
2    15.9129     79.74  
3    15.9129     79.74  
4    15.9129     79.74
In [4]:
data.info
Out[4]:
<bound method DataFrame.info of              Region         Date  Frequency   Estimated Unemployment Rate (%)  \
0    Andhra Pradesh   31-01-2020          M                              5.48   
1    Andhra Pradesh   29-02-2020          M                              5.83   
2    Andhra Pradesh   31-03-2020          M                              5.79   
3    Andhra Pradesh   30-04-2020          M                             20.51   
4    Andhra Pradesh   31-05-2020          M                             17.43   
..              ...          ...        ...                               ...   
262     West Bengal   30-06-2020          M                              7.29   
263     West Bengal   31-07-2020          M                              6.83   
264     West Bengal   31-08-2020          M                             14.87   
265     West Bengal   30-09-2020          M                              9.35   
266     West Bengal   31-10-2020          M                              9.98   

      Estimated Employed   Estimated Labour Participation Rate (%) Region.1  \
0               16635535                                     41.02    South   
1               16545652                                     40.90    South   
2               15881197                                     39.18    South   
3               11336911                                     33.10    South   
4               12988845                                     36.46    South   
..                   ...                                       ...      ...   
262             30726310                                     40.39     East   
263             35372506                                     46.17     East   
264             33298644                                     47.48     East   
265             35707239                                     47.73     East   
266             33962549                                     45.63     East   

     longitude  latitude  
0      15.9129    79.740  
1      15.9129    79.740  
2      15.9129    79.740  
3      15.9129    79.740  
4      15.9129    79.740  
..         ...       ...  
262    22.9868    87.855  
263    22.9868    87.855  
264    22.9868    87.855  
265    22.9868    87.855  
266    22.9868    87.855  

[267 rows x 9 columns]>
In [5]:
print(data.describe)

<bound method NDFrame.describe of              Region         Date  Frequency   Estimated Unemployment Rate (%)  \
0    Andhra Pradesh   31-01-2020          M                              5.48   
1    Andhra Pradesh   29-02-2020          M                              5.83   
2    Andhra Pradesh   31-03-2020          M                              5.79   
3    Andhra Pradesh   30-04-2020          M                             20.51   
4    Andhra Pradesh   31-05-2020          M                             17.43   
..              ...          ...        ...                               ...   
262     West Bengal   30-06-2020          M                              7.29   
263     West Bengal   31-07-2020          M                              6.83   
264     West Bengal   31-08-2020          M                             14.87   
265     West Bengal   30-09-2020          M                              9.35   
266     West Bengal   31-10-2020          M                              9.98   

      Estimated Employed   Estimated Labour Participation Rate (%) Region.1  \
0               16635535                                     41.02    South   
1               16545652                                     40.90    South   
2               15881197                                     39.18    South   
3               11336911                                     33.10    South   
4               12988845                                     36.46    South   
..                   ...                                       ...      ...   
262             30726310                                     40.39     East   
263             35372506                                     46.17     East   
264             33298644                                     47.48     East   
265             35707239                                     47.73     East   
266             33962549                                     45.63     East   

     longitude  latitude  
0      15.9129    79.740  
1      15.9129    79.740  
2      15.9129    79.740  
3      15.9129    79.740  
4      15.9129    79.740  
..         ...       ...  
262    22.9868    87.855  
263    22.9868    87.855  
264    22.9868    87.855  
265    22.9868    87.855  
266    22.9868    87.855  

[267 rows x 9 columns]>

see if this dataset contains missing values or not

In [6]:
print(data.isnull().sum())

Region                                      0
 Date                                       0
 Frequency                                  0
 Estimated Unemployment Rate (%)            0
 Estimated Employed                         0
 Estimated Labour Participation Rate (%)    0
Region.1                                    0
longitude                                   0
latitude                                    0
dtype: int64

rename all the columns.

In [7]:
data.colums =["states","Date","Frequency",
              "Estimated Unemployment Rate","Estimated Employed",
              "Estimated Labour Participation Rate","Region",
              "longitude","latitude"]

C:\Users\pavan\AppData\Local\Temp\ipykernel_4548\1947871789.py:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
  data.colums =["states","Date","Frequency",
In [8]:
print(data)

             Region         Date  Frequency   Estimated Unemployment Rate (%)  \
0    Andhra Pradesh   31-01-2020          M                              5.48   
1    Andhra Pradesh   29-02-2020          M                              5.83   
2    Andhra Pradesh   31-03-2020          M                              5.79   
3    Andhra Pradesh   30-04-2020          M                             20.51   
4    Andhra Pradesh   31-05-2020          M                             17.43   
..              ...          ...        ...                               ...   
262     West Bengal   30-06-2020          M                              7.29   
263     West Bengal   31-07-2020          M                              6.83   
264     West Bengal   31-08-2020          M                             14.87   
265     West Bengal   30-09-2020          M                              9.35   
266     West Bengal   31-10-2020          M                              9.98   

      Estimated Employed   Estimated Labour Participation Rate (%) Region.1  \
0               16635535                                     41.02    South   
1               16545652                                     40.90    South   
2               15881197                                     39.18    South   
3               11336911                                     33.10    South   
4               12988845                                     36.46    South   
..                   ...                                       ...      ...   
262             30726310                                     40.39     East   
263             35372506                                     46.17     East   
264             33298644                                     47.48     East   
265             35707239                                     47.73     East   
266             33962549                                     45.63     East   

     longitude  latitude  
0      15.9129    79.740  
1      15.9129    79.740  
2      15.9129    79.740  
3      15.9129    79.740  
4      15.9129    79.740  
..         ...       ...  
262    22.9868    87.855  
263    22.9868    87.855  
264    22.9868    87.855  
265    22.9868    87.855  
266    22.9868    87.855  

[267 rows x 9 columns]

Look at the correlation b/w the features of this dataset.

In [9]:
plt.style.use("seaborn-whitegrid")
plt.figure(figsize=(12,10))
sns.heatmap(data.corr())
plt.show()

C:\Users\pavan\AppData\Local\Temp\ipykernel_4548\221186264.py:1: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.
  plt.style.use("seaborn-whitegrid")
C:\Users\pavan\AppData\Local\Temp\ipykernel_4548\221186264.py:3: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  sns.heatmap(data.corr())

Look at the estimated number of employess according to different regions of india

In [10]:
data.columns =["states","Date","Frequency",
               "Estimated Unemployment Rate","Estimated Employed",
               "Estimated Labour Participation Rate","Region",
                "longitude","latitude"]
plt.title("Indian Unemployment")
sns.histplot(x="Estimated Employed",hue="Region",data=data)
plt.show()

see the unemployment rate according to differentregion of india

In [11]:
plt.figure(figsize=(12,10))
plt.figure("Indian Unemployment")
sns.histplot(x="Estimated Unemployment Rate",hue="Region",data=data)
plt.show()

<Figure size 1200x1000 with 0 Axes>

create a dashboard ti analyze the unemployment rate of each Indian state by region.use sunburst plot.

In [12]:
unemploment = data[["states","Region","Estimated Unemployment Rate"]]
figure = px.sunburst(unemploment,path=["Region","states"],
                    values="Estimated Unemployment Rate",
                     width =700,height=700, color_continuous_scale="RdY1Gn",
                     title="Unemployment Rate in Indian")
figure.show()